What Data Looks Like for This Use Case
Data for this use case originates from engineering order systems, sand, cement and admixture testing equipment, aggregate moisture content measuring instruments, mixing plant production control systems, and project testing laboratories. Data updates occur per production batch per truck, per raw material batch, and in daily summaries. Daily documentation includes mix proportion notices, opening identification records, production and delivery slips, raw material inspection reports, test block retention records, and strength test reports. Fields cover raw material batch number, measured moisture content, mixing parameters, slump index, test block strength grade, transportation duration, and no pre-set additional statistical values.
How These Characteristics Impact Tool Calling and Plugins
Data updates follow single production batches and single raw material batches. Tool calling must support pulling production and inspection data for a single batch at a time to avoid cross-batch data confusion. Multi-source data collection requirements mean plugins must adapt to API formats of different systems. Separate request parameter configurations are required when connecting to mixing plant control systems and laboratory testing systems. Specific fields defined in business documents require tool calling to explicitly target extraction fields such as slump and test block strength grade. Generic text extraction alone cannot deliver information that meets business requirements. The high-frequency update feature also requires tool calling context caching to be isolated by production batch, preventing old data from interfering with current audit processes.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | ["functionCall"] | Mandatorily call specified tools to complete inspection record audits, avoid generic Q&A interfering with business processes, comply with FastGPT v4.17.0 tool calling rules |
| Workflow/API function-call configuration | {"name": "extract_concrete_check_data", "parameters": ["batch_id", "slump", "strength"]} | Extract data for specific business fields related to ready-mixed concrete, ensure key information required for audits is complete |
| Single request timeout | 60–90 seconds | Single-batch ready-mixed concrete production data volume is small. An overly long timeout will occupy system resources, while an overly short timeout will cause failed incomplete data pulls |
| Data source connection configuration | Set based on actual calibration | API formats of different mixing plant control systems and laboratory testing systems vary. Adjust request headers and parameter formats based on the actual connected systems |
| Batch isolation switch | Enabled | Each truck's production data is updated independently. Isolating batches prevents cross-batch data confusion and ensures accurate audit results |
Three Common Misconfigurations
- Calling a text extraction tool returns a 400 Invalid JSON payload. Unknown name error. The cause is that passed parameters include field names not defined by the plugin, and the preset field list of the business document is not strictly matched.
- Calling a workflow API fails to retrieve preset context information. The cause is that the request does not carry a session identifier parameter, or the workflow's context transfer configuration is not enabled.
- Tool call results do not include test block strength data. The cause is that the field extraction rule does not specify the extraction path for the test block strength grade, and the key business field is omitted.
How to Verify Correct Configuration
- Initiate a tool call request for single-batch ready-mixed concrete production data, check if the returned result includes preset business fields such as batch number, slump, and test block strength grade.
- Call the workflow API with a session identifier, check if the returned result includes context information from historical sessions.
- View tool call logs, confirm that request parameters do not include undefined fields, and no 400-level errors are returned.
- Test call requests for different production batches, confirm that cross-batch data confusion does not appear in the results.
The values provided here are common starting points, and should be evaluated against the reader's own samples.